一个改进的卡尔曼过算法,用于紧密的GNSS/INS集成导航系统
Yuelin Yuan1, Fei Li2, Jialiang Chen2
1School of Electronic Science, National University of Defense Technology, Changsha 410005, China.
Mathematical biosciences and engineering : MBE
|February 2, 2024
概括
本研究介绍了一种使用单数值分解 (SVD) 来实现全球导航卫星系统/惯性导航系统 (GNSS/INS) 集成的改进强大的卡尔曼波器. 该方法通过动态纠正噪声共变性来提高稳定性和定位准确性,大大减少错误.
科学领域:
- 导航系统工程 导航系统工程
- 信号处理 信号处理
- 强大的估计理论.
背景情况:
- 使用单值分解 (SVD) 的卡尔曼过器在各种计算应用中有效地减轻数值圆形错误.
- 紧密结合的全球导航卫星系统 (GNSS) 和惯性导航系统 (INS) 集成要求提高过性能和适应性.
- 传统方法因固定噪声共变而难以应对波动的GNSS信号,从而限制了集成导航的准确性.
研究的目的:
- 为紧密合的GNSS/INS导航系统提出一个改进的可靠方法.
- 通过解决固定噪声共变量的局限性来提高过性能和适应性.
- 通过其SVD形式扩展信息过器 (IF) 的应用.
主要方法:
- 开发了一种新的强大方法,使用来自创新的校正变量和SVD衍生的矩阵动态纠正噪声共变量.
- 在原始矩阵上通过SVD构建了一个新的矩阵,以提高强度.
- 导出了信息过器 (IF) 的 SVD 形式,以扩大其适用性.
主要成果:
- 与传统的基于SVD的卡尔曼过器相比,拟议的方法显示出更高的稳定性和更高的定位精度.
- 与传统的基于SVD的卡尔曼算法相比,最大误差减少了45.77%.
- 与传统的IF算法相比,SVD形式的IF算法实现了根平均平方误差的4.7%降低.
结论:
- 拟议的强大方法在紧密合的GNSS/INS导航中显著提高了定位准确性和稳定性.
- 通过SVD进行动态噪声协差校正,可以更好地适应信号波动.
- 基于SVD的IF提供了更准确,更可靠的导航解决方案,通过模拟和实验验证.
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